P1: Use of Small Unmanned Aircraft System Data for Transportation Infrastructure Inspections

Overview

The use of small unmanned aircraft systems (sUAS) or small unmanned aerial vehicles (sUAV), more commonly referred to as drones, equipped with a variety of sensors, has been experiencing significant growth in many application areas. However, there are research needs to be addressed to understand the use and value of data collected with sUAS to achieve the beneficial uses of this innovative technology for inspecting transportation infrastructure systems (such as pavements, bridges, culvert, and on). The aim of this project is to develop recommended analysis processes and procedures to detect and classify various pavement distress types (e.g., cracks, disintegration, distortion) using image data collected by various sUAS-mounted sensors.

Student Participation

LAUNCH students will work with the Postdoctoral Research Associates, research staff, and faculty in the Program for Sustainable Pavement Engineering and Research (PROSPER) at ISU in analyzing various pavement distress image data by using geographic information system (GIS) and remote sensing software and image analysis tools and developing advanced techniques and algorithms to be converted into 3D models and large orthomosaic map/photo. Students will use a vast amount of distress image data collected by various sUAS-mounted sensors such as optical camera, thermal camera, multispectral/hyperspectral camera, and Light Detection and Ranging (LiDAR) for this work. Therefore, LAUNCH students can develop the comprehensive research experience in sUAV data analysis.

Prerequisites:

Prior experience using GIS and Remote Sensing software (e.g., ArcGIS Desktop, QGIS, ILWIS, ERDAS Imagine) and image analysis tools (e.g., Python or MATLAB based analysis tools.) 

Project Mentor: Dr. Halil Ceylan